DIAL: Position-Debiased LLM Judges with Adaptive Human Preference Calibration
DIAL is introduced, a unified framework that combines abundant LLM comparisons with limited human comparisons to separate judge-specific position effects, learn shared structure in position-debiased LLM preferences, and adaptively calibrate that structure toward the human preference target.
Ze-Sheng Cai, Ying-Qi Fan, Si-Chang Chen et al.
· 0 citations